[1888] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[3452] | 3 | * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[1888] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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[4068] | 25 | using HeuristicLab.Common;
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[1888] | 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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[3452] | 28 | using HeuristicLab.Parameters;
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[5275] | 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[1888] | 30 |
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[3452] | 31 | namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
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[1888] | 32 | /// <summary>
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| 33 | /// The Variance Accounted For (VAF) function calculates is computed as
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[3452] | 34 | /// VAF(y,y') = 1 - var(y-y')/var(y)
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[1888] | 35 | /// where y' denotes the predicted / modelled values for y and var(x) the variance of a signal x.
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| 36 | /// </summary>
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[3452] | 37 | public class SimpleVarianceAccountedForEvaluator : SimpleEvaluator {
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[1888] | 38 |
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[3452] | 39 | public ILookupParameter<DoubleValue> VarianceAccountedForParameter {
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| 40 | get { return (ILookupParameter<DoubleValue>)Parameters["VarianceAccountedFor"]; }
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[1888] | 41 | }
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| 42 |
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[5275] | 43 | [StorableConstructor]
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| 44 | protected SimpleVarianceAccountedForEvaluator(bool deserializing) : base(deserializing) { }
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| 45 | protected SimpleVarianceAccountedForEvaluator(SimpleVarianceAccountedForEvaluator original, Cloner cloner)
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| 46 | : base(original, cloner) {
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| 47 | }
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| 48 | public override IDeepCloneable Clone(Cloner cloner) {
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| 49 | return new SimpleVarianceAccountedForEvaluator(this, cloner);
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| 50 | }
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[3452] | 51 | public SimpleVarianceAccountedForEvaluator() {
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| 52 | Parameters.Add(new LookupParameter<DoubleValue>("VarianceAccountedFor", "The variance of the original values accounted for by the estimated values (VAF(y,y') = 1 - var(y-y') / var(y) )."));
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[1888] | 53 | }
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| 54 |
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[3452] | 55 | protected override void Apply(DoubleMatrix values) {
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| 56 | var original = from i in Enumerable.Range(0, values.Rows)
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| 57 | select values[i, ORIGINAL_INDEX];
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| 58 | var estimated = from i in Enumerable.Range(0, values.Rows)
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| 59 | select values[i, ESTIMATION_INDEX];
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| 60 | VarianceAccountedForParameter.ActualValue = new DoubleValue(Calculate(original, estimated));
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| 61 | }
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| 62 |
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| 63 | public static double Calculate(IEnumerable<double> original, IEnumerable<double> estimated) {
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| 64 | var originalEnumerator = original.GetEnumerator();
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| 65 | var estimatedEnumerator = estimated.GetEnumerator();
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| 66 | var errors = new List<double>();
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| 67 | while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
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| 68 | double e = estimatedEnumerator.Current;
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| 69 | double o = originalEnumerator.Current;
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| 70 | if (!double.IsNaN(e) && !double.IsInfinity(e) &&
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| 71 | !double.IsNaN(o) && !double.IsInfinity(o)) {
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| 72 | errors.Add(o - e);
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[1888] | 73 | }
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| 74 | }
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[3452] | 75 | if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext()) {
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| 76 | throw new ArgumentException("Number of elements in original and estimated enumeration doesn't match.");
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| 77 | }
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| 78 |
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| 79 | double errorsVariance = errors.Variance();
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| 80 | double originalsVariance = original.Variance();
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[2226] | 81 | if (originalsVariance.IsAlmost(0.0))
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| 82 | if (errorsVariance.IsAlmost(0.0)) {
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[2136] | 83 | return 1.0;
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| 84 | } else {
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[3452] | 85 | return double.MaxValue;
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[2136] | 86 | } else {
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| 87 | return 1.0 - errorsVariance / originalsVariance;
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| 88 | }
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[1888] | 89 | }
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| 90 | }
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| 91 | }
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